- Construction of Time-series Displacement Data of Yongdam Dam Based on PSInSAR Analysis of Satellite C-band SAR Images
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Taewook Kim, Hyunjin Shin, Jungkyo Jung, Hyangsun Han, Ki-mook Kang, Euiho Hwang
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GEO DATA. 2023;5(3):147-154. Published online September 22, 2023
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DOI: https://doi.org/10.22761/GD.2023.0024
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 Abstract
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- The increase in water-related disasters due to climate change has a significant impact on the stability of water resource facilities. The displacement of a water resource facility is one of the important indicators to evaluate the stability of the facility. In this study, the time-series displacement of the Yongdam Dam was constructed by applying the persistent scatter interferometric synthetic aperture radar (PSInSAR) technique to the Sentinel-1 C-band SAR images. A sufficient number of persistent scatterers were derived to enable local deformation monitoring of the Yongdam Dam, and the dam showed very small displacement velocity except during the heavy rainfall in August 2020. In the future, C-band SAR imagery from the water resources satellite (Next Generation Medium Satellite 5) is expected to provide accurate displacement data for water resource facilities.
- Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion
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Joonhyuk Choi, Ki-mook Kang, Euiho Hwang
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GEO DATA. 2023;5(3):177-184. Published online September 27, 2023
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DOI: https://doi.org/10.22761/GD.2023.0029
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Abstract
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- For the spatiotemporal analysis of water resources and disasters, water body detection using satellite imagery is crucial. Recently, AI-based methods have been widely employed in water body detection using satellite imagery. To use these AI techniques, a substantial amount of training data is required. When creating training data for water body detection, optical imagery and synthetic aperture radar (SAR) imagery have their respective strengths and weaknesses. To use the advantages of both, this study proposes a water body detection method through the fusion of optical and SAR imagery. The results of the proposed model show an Intersection over Union of 0.612 and an F1 score of 0.759, which is better compared to using either optical or SAR imagery alone. This research presents a method that can easily generate a large amount of water body data, making it promising for use as AI training data for water body detection.
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